Senior Data Scientist

Leverage Generative AI and machine learning to improve customer experiences at a Fortune 500 transportation company .

Join a collaborative, high-performing ML/AI team supporting Contact Center and Digital Customer Experience initiatives. This role will focus on designing, developing, testing, and productionizing GenAI applications, including customer comment and complaint classification, interaction summarization, and tailored customer response generation. The ideal candidate is a hands-on technologist who can take AI solutions from prototype through deployment while improving existing codebases and delivering scalable, production-ready systems.

Contract Duration: Long term

Work Arrangement

• Hybrid schedule: onsite Tuesday through Thursday
• Remote work on Monday and Friday
• Candidates must be available to work onsite in Fort Worth, Texas, on the first day of the assignment

Required Skills & Experience

• Master’s or Ph.D. degree in Data Science, Machine Learning, Computer Science, Applied Mathematics, Statistics, or a related quantitative discipline

• 5 years of experience in data science, machine learning engineering, AI engineering, or a related field

• Strong proficiency in Python, including:

• Production-grade coding, Modularization, Testing, Debugging, Performance tuning

• Hands-on experience designing and implementing Generative AI applications

• Experience with LLMs, prompt engineering, and AI application development

• Experience developing and productionizing ML/AI pipelines, including:

• Model deployment, Orchestration, Monitoring, Optimization

• Strong understanding of statistical and machine learning techniques

• Proficiency with SQL and working with data

• Strong written and verbal communication skills

• Ability to analyze complex problems, identify solutions, and collaborate effectively across diverse teams

Desired Skills & Experience

• Experience with agentic AI and AI application architecture
• Experience with LLM-as-a-Judge or other LLM evaluation techniques
• Experience building customer-facing Generative AI applications
• Knowledge of AI guardrails, privacy, cybersecurity, and responsible AI practices
• Experience with Azure ML and/or Databricks
• Familiarity with ML lifecycle tools, including MLflow, CI/CD, and model monitoring
• Experience containerizing and deploying machine learning applications
• Experience improving existing codebases and integrating AI systems with enterprise applications
• Microsoft Dynamics experience

What You Will Be Doing

• Build and productionize Generative AI applications for customer service and contact center use cases
• Develop solutions for customer comment and complaint classification, summarization, and empathetic response generation
• Design, test, deploy, monitor, and optimize machine learning and AI pipelines
• Apply prompt engineering, LLM evaluation, classifiers, and other GenAI techniques
• Support human-in-the-loop workflows in which AI-generated responses receive human review
• Contribute to experiments involving agentic AI and alternative AI application architectures
• Collaborate with data scientists, data engineers, product partners, and other stakeholders
• Prepare and transform data for use in ML and GenAI applications
• Debug technical issues and optimize application performance
• Work within existing codebases while also developing new applications
• Apply appropriate security, privacy, and responsible AI considerations
• Communicate technical concepts and project status to technical and nontechnical audiences

Tech Breakdown

• Python – Primary development language
• Generative AI, LLMs, and prompt engineering
• Machine learning and AI pipelines
• SQL and data processing
• Azure ML and Databricks – Preferred
• MLflow, CI/CD, and model monitoring – Preferred
• Microsoft Dynamics – Preferred

Daily Responsibilities

• Hands-on development of Python-based GenAI and machine learning applications
• Design and implementation of AI pipelines and production workflows
• Data preparation, testing, deployment, monitoring, debugging, and optimization
• Collaboration with data scientists, data engineers, product partners, and business stakeholders
• Participation in technical discussions, code reviews, and solution design
• Improvement and maintenance of existing AI and machine learning codebases